book

Machine Learning for Time-Series with Python

Name: Machine Learning for Time-Series with Python
Author: Ben Auffarth
ISBN: 9781801819626

by Ben Auffarth

October 2021

Beginner to intermediate

370 pages

8h 19m

English

Packt Publishing

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Preface
Who this book is forWhat this book coversTo get the most out of this bookGet in touch
Introduction to Time-Series with Python
What Is a Time-Series?Characteristics of Time-SeriesTime-Series and Forecasting – Past and PresentDemographyGeneticsAstronomyEconomicsMeteorologyMedicineApplied StatisticsPython for Time-SeriesInstalling librariesJupyter Notebook and JupyterLabNumPypandasBest practice in PythonSummary
Time-Series Analysis with Python
What is time-series analysis?Working with time-series in PythonRequirementsDatetimepandasUnderstanding the variablesUncovering relationships between variablesIdentifying trend and seasonalitySummary
Preprocessing Time-Series
What Is Preprocessing?Feature TransformsScalingLog and Power TransformationsImputationFeature EngineeringDate- and Time-Related FeaturesROCKETShapeletsPython PracticeLog and Power Transformations in PracticeImputationHoliday FeaturesDate AnnotationPaydaysSeasonsThe Sun and MoonBusiness DaysAutomated Feature ExtractionROCKETShapelets in PracticeSummary
Introduction to Machine Learning for Time-Series
Machine learning with time-seriesSupervised, unsupervised, and reinforcement learning History of machine learningMachine learning workflowCross-validation Error metrics for time-seriesRegressionClassificationComparing time-seriesMachine learning algorithms for time-seriesDistance-based approachesShapeletsROCKETTime-Series Forest and Canonical Interval ForestSymbolic approachesHIVE-COTEDiscussionImplementationsSummary
Forecasting with Moving Averages and Autoregressive Models
What are classical models?Moving average and autoregressionModel selection and orderExponential smoothingARCH and GARCHVector autoregressionPython librariesStatsmodelsPython practiceRequirementsModeling in PythonSummary
Unsupervised Methods for Time-Series
Unsupervised methods for time-seriesAnomaly detectionMicrosoftGoogleAmazonFacebookTwitterImplementationsChange point detectionClusteringPython practiceRequirementsAnomaly detectionChange point detectionSummary
Machine Learning Models for Time-Series
More machine learning methods for time-seriesValidationK-nearest neighbors with dynamic time warpingSilverkiteGradient boostingPython exerciseVirtual environmentsK-nearest neighbors with dynamic time warping in PythonSilverkiteGradient boostingEnsembles with KatsSummary
Online Learning for Time-Series
Online learning for time-seriesOnline algorithmsDriftDrift detection methodsAdaptive learning methodsPython practiceDrift detectionRegressionModel selectionSummary
Probabilistic Models for Time-Series
Probabilistic Models for Time-SeriesProphetMarkov ModelsFuzzy ModelingBayesian Structural Time-Series ModelsPython ExerciseProphetMarkov Switching ModelFuzzy Time-SeriesBayesian Structural Time-Series ModelingSummary

Deep Learning for Time-Series
Introduction to deep learningDeep learning for time-seriesAutoencodersInceptionTimeDeepARN-BEATSRecurrent neural networksConvNetsTransformer architecturesInformerPython practiceFully connected networkRecurrent neural networkDilated causal convolutional neural networkSummary
Reinforcement Learning for Time-Series
Introduction to reinforcement learningReinforcement Learning for Time-SeriesBandit algorithmsDeep Q-LearningPython PracticeRecommendationsTrading with DQNSummary
Multivariate Forecasting
Forecasting a Multivariate Time-SeriesPython practiceWhat's next for time-series?
Other Books You May Enjoy
Index

Content preview from Machine Learning for Time-Series with Python

2 Time-Series Analysis with Python

Time-Series analysis revolves around getting familiar with a dataset and coming up with ideas and hypotheses. It can be thought of as "storytelling for data scientists" and is a critical step in machine learning, because it can inform and help shape tentative conclusions to test while training a machine learning model. Roughly speaking, the main difference between time-series analysis and machine learning is that time-series analysis does not include formal statistical modeling and inference.

While it can be daunting and seem complex, it is a generally very structured process. In this chapter, we will go through the fundamentals in Python for dealing with time-series patterns. In Python, we can do time-series ...

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Machine Learning for Time-Series with Python

by Ben Auffarth

2

Time-Series Analysis with Python

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.